BIG DATA PROCESSING WITH PYTHON IN SOCIAL NETWORKS

Social networks generate a huge amount of data about the behavior, interests, preferences and connections of users, which can be used for various purposes such as marketing, advertising, analytics, research, etc. However, these data have a large volume, variety and update rate, and require special methods and tools for their analysis and processing. Python is a high-level programming language that has many advantages for working with big data, such as simplicity and flexibility of syntax, a rich set of libraries and frameworks for data processing and analysis, support for multithreading and distributed computing, open source code, and a large community of developers and users.

The article shows how to use Python to parse and collect data from various sources, to clean and pre-process data, such as removing duplicates, gaps, outliers and noise, normalizing and standardizing data, encoding categorical features, etc., for analysis and data visualization, such as building statistical characteristics, graphs, charts and maps to study the distribution, correlation and dependence of data, for machine learning and data mining.

Keywords: Python, big data, social networks, data parsing, data analysis, machine learning.